Analytics in action
More than meets the eye: 3 people-focused keys to self-service analytics success
Luke Komiskey | February 18, 2015
Organizations are up to their ears in data.
To keep pace with the speed of business, they’re turning to self-service data analytics. Data discovery tools such as Tableau and Qlik are enabling business users to access, explore, and visualize data that was previously available to only tech-savvy IT departments.
Self-service data analytics is fun, exciting, and flashy; however, the implementation of these self-service tools is often a big change for an organization and its people. While success stories from this new BI paradigm can be found everywhere, we rarely hear about the admittedly less fun, exciting, and flashy aspects that are critical to success: change and adoption efforts.
Why? According to Towers Watson, when it comes to change initiatives, such as implementing self-service analytics, “the key difference between success and failure is most often related to the lack of change management—employees neither ready for, nor engaged in, the change.” Implementing self-service analytics takes thoughtful change management measures and a focus on people. Organizations often underestimate the extent of change stemming from rapid growth and end-user appetite for self-service analytics.
While success with self-service isn’t guaranteed, it is possible. With that in mind, here are three tips to successfully implement and sustain self-service analytics.
1. Identify sponsors early
A strong self-service analytics business case must consider and resonate with all levels of the organization—from project leadership and sponsoring executives to the end user. Knowing who your stakeholder groups are, why they care, and how they will be impacted by the change will help ensure a successful, sustainable self-service analytics solution. In particular, engage executive leadership early as their sponsorship will add momentum to the deployment.
Self-service initiatives are unique in that they often spawn from frustrated business users, but require collaboration with broader teams and IT to ensure continued success. Though deployments will initially consist of business advocates and executive sponsorship, ensure that future end users, IT teams, and other executives are also on board early with the solution. Engage teams too late and you’ll not only blindside them, but you’ll also run the risk of short-sighted design.
Because end users directly drive adoption of the new self-service analytics approach, they need to see and understand the business value, lest it become just another tool in their growing toolbox. Strong sponsorship ultimately markets the benefits of self-service analytics internally.
2. Recognize and support end users
It’s important to understand and recognize the change impacts to the end users when implementing a self-service analytics tool. Every end user will approach the change with differing experiences and perspectives; the key is understanding their starting point so you can bring them along on the organizational change.
The transition from static reporting to visual data exploration isn’t seamless—both information consumers and data analysts must strike a balance between usability and insightfulness within visualizations. Though the benefits of data discovery is supported by National Visualization and Analytics Center research—it found that human sight can comprehend at the speed of modern computer networks—an organization can’t assume that end users will equally adjust to changes in their approach to data analysis. Let’s face it: People love their spreadsheets.
One thing is guaranteed with self-service analytics: Data analysts need to be prepared for additional business questions as self-service BI dramatically speeds up development cycles. (That’s a good thing!)
It is also important to recognize that different stakeholders will require varying levels of knowledge and commitment throughout a self-service transition. Looking at stakeholders through the lens of the Prosci ADKAR model “provides a simple and action-oriented framework for [understanding and] taking control of change.”
To drive a successful, sustainable self-serve initiatives, remember to recognize the change impacts to end users, understand where they are on the change curve, and identify where they need to be.
3. Invest in people
Self-service implementations can be large organizational investments, but sustainability requires an equal investment in people.
A new analytics tool impacts how end users access information and make business decisions. Ultimately, users want to feel connected to the business case and know that they are driving better, faster decision making that directly impacts the business. Incorporating investments for your people—such as training, support networks, and ongoing collaboration—goes a long way to help foster an excited community that’s committed to the broader self-service initiative.
Take the time to consider a few key questions:
- Does this change warrant a super user, or champion, network to support and drive adoption?
- How will you train your end users on the new analytics tool?
- What ongoing resources and/or collaboration tools will you provide to promote long-term sustainability?
Self-service analytics success stories often focus on bottom-line dollar savings from business insights. But they tend to be one-off successes that fail to paint the full picture of the entire organization’s adoption of the new BI paradigm. Successful implementations require more than a technology investment. Invest in your people through a comprehensive stakeholder analysis, clear change impact assessment, and ongoing end user support.
There’s more than meets the eye when it comes to self-service analytics. But, focusing on these three people-focused tips can help unlock business value by engaging users and enabling them to explore data like never before.
Luke Komiskey consults for the information management & analytics practice in Minneapolis. He loves helping local clients explore their data and answer business questions through data discovery and advanced analytics. Follow Luke on Twitter: @Luke_Komiskey.